A Pattern Recognition System for Detecting Use of Mobile Phones While Driving (original) (raw)
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Analysis and Development of a Novel Algorithm for the In-vehicle Hand-Usage of a Smartphone
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018
Smartphone usage while driving is unanimously considered to be a really dangerous habit due to strong correlation with road accidents. In this paper, the problem of detecting whether the driver is using the phone during a trip is addressed. To do this, high-frequency data from the triaxial inertial measurement unit (IMU) integrated in almost all modern phone is processed without relying on external inputs so as to provide a self-contained approach. By resorting to a frequency-domain analysis, it is possible to extract from the raw signals the useful information needed to detect when the driver is using the phone, without being affected by the effects that vehicle motion has on the same signals. The selected features are used to train a Support Vector Machine (SVM) algorithm. The performance of the proposed approach are analyzed and tested on experimental data collected during mixed naturalistic driving scenarios, proving the effectiveness of the proposed approach.
Mobile Phone Detection and Notification for The Prevention of Car Accidents
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The use of mobile devices can easily divert a driver's attention away from the road. Dangerous driving, such as texting and driving, can cause havoc in traffic and jeopardize safety. The goal of this project is to develop an accident-avoidance system that can detect the presence of a mobile phone in the driver's hand by installing an in-car camera facing the driver and running a YOLOv3-Tiny algorithm for mobile phone detection. In addition, the model will issue an audio alert to the driver and use face detection algorithms to determine the driver's identity. Twilio APIs are being used to send live messages to car owners about the actions taken using the car's location information.
Driver Cell Phone Usage Detection from HOV/HOT NIR Images
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014
Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front windshield. The developed method consists of two stages; first, we localize the driver's face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.
Smartphones, Suitable Tool for Driver Behavior Recognition. A Systematic Review
Communications in Computer and Information Science, 2020
A current reality is the increase in the number of road traffic accidents caused mainly by incorrect driving habits. For this reason, the development of different approaches that can help reduce accidents on the road is imperative. A strategy is the use of smartphones as a tool to identify driving behaviors, which is documented in the state of the art. This paper presents a systematic review focused on the strategies used to recognizing driving behaviors with sensors that are part of smartphones. The review was carried out on the Scopus database, included studies published in the last 4 years (2017-2020) that allowed identifying a total of 222222 relevant results. This paper presents a report of the most used sensors, algorithms, driving events and driving patterns. It includes result discussion and considerations of future work on this topic, additional to the bibliometric report.
Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking, 2018
Integration of the physical world with the computerized world has led to the manifestation of Cyber-Physical Systems (CPSs) in an attempt to build a better and smarter world. In this paper, such a CPS named D&RSense has been proposed to promote smart transportation in order to make travelling more comfortable and safe. By studying driving patterns of drivers, D&RSense can get valuable insights to their braking and accelerating styles which can help to give them real-time warnings when they drive aggressively. Detection of rash driving prone areas across the city can help to recommend which areas of the city need stricter surveillance. D&RSense involves smartphones of commuters and utilizes their accelerometer and GPS sensors to detect driving events like braking and acceleration as well as poor road conditions like bumps and potholes by applying the ensemble learning method for classification, Random Forest (RF). The accuracy of the same has been compared to other supervised machine learning classifiers like Naïve Bayes, k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). Rash-driving prone areas and poor road segments during the course of the experiment have been plotted using Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Effectiveness of the proposed application has been evaluated through extensive testing.
Sensing vehicle dynamics for determining driver phone use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services, 2013
This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over 90% with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., 95%) with a lower false positive rate.
Identifying the transportation mode can offer several advantages in different fields of transportation engineering such as transportation planning and intelligent transportation systems which lead to a broad range of environmental and safety applications. Support vector machine, as a supervised learning method, is adopted in this paper to develop a multi-class classifier to distinguish between different transportation modes including driving a car, riding a bicycle, taking a bus, walking, and running. Data from different mobile phone sensors were trained and tested to evaluate the model. Sensors from which the data were obtained include accelerometer, gyroscope, rotation vector, and Global Positioning System (GPS). A Gaussian kernel was applied as part of the classifier and unlike some ambiguity seen in the literature, a complete model selection is conducted. A small window size of one second was considered, so the model can be useful in a broader range of applications. For the first time, the data from gyroscope and rotation vector sensors were used in experiments based on individual sensor data. The study showed that such data can contribute to high detection rates. It was found that including attributes that have similar behavior among different modes can negatively impacts the detection rates. When using multiple sensors, high average overall accuracies of 98.86% and 97.89% were achieved with and without using the GPS data, respectively. These results offer improvements compared to what is reported in the literature. The bus mode was the most difficult mode to differentiate due to some similarities to the car and the bike mode data.
Automatically identifying a mobile phone user's position within a vehicle
Cornell University - arXiv, 2021
Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to passively detect the location of the phone's user within a vehicle. In a large, varied dataset we were able correctly identify if the user was in the driver's seat or one of the passenger seats with 94.9% accuracy. This model could be used by application developers to selectively change or lock functionality while a user is driving, but not if the user is a passenger in a moving vehicle.
Driving event recognition using machine learning and smartphones
F1000Research, 2022
Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone's built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add "noise" to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that the proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver's driving behavior.
Driver Identification by Neural Network on Extracted Statistical Features from Smartphone Data
2020
The future of transportation is driven by the use of artificial intelligence to improve living and transportation. This paper presents a neural network-based system for driver identification using data collected by a smartphone. This system identifies the driver automatically, reliably and in real-time without the need for facial recognition and also does not violate privacy. The system architecture consists of three modules data collection, preprocessing and identification. In the data collection module, the data of the accelerometer and gyroscope sensors are collected using a smartphone. The preprocessing module includes noise removal, data cleaning, and segmentation. In this module, lost values will be retrieved and data of stopped vehicle will be deleted. Finally, effective statistical properties are extracted from data-windows. In the identification module, machine learning algorithms are used to identify drivers' patterns. According to experiments, the best algorithm for d...